Hearing me hearing you

Fusaroli, Weed, Fein & Naigles
2019

Language learning: it's complicated!

  1. Child-based factors
  2. Environmental factors

Which factors do researchers focus on?



TD children

  • Environmental factors
  1. Quantity of parents' input
  2. Quality of parents' input
  3. Socio-economic status
  4. etc..



Children with ASD

  • Child-based factors
  1. IQ
  2. Symptom severity
  3. Expressive language measures
  4. Social skills measures
  5. etc..

Goal

Try to model as much of the complexity as we can.

Our 6 research questions

  1. What do the longitudinal trajectories of children's language development look like?
  2. What do the longitudinal trajectories of parents' language production look like?
  3. Do parents and children linguistically match each other in conversation?
  4. Which factors best predict child linguistic development?
  5. Are child-based or environmental factors more important to children' linguistic development (and how is this mediated by diagnosis)?
  6. Do children and adults influence each other's later linguistic productions?

Participants

Variable TD Mean (95% CI) ASD Mean β (SE) t/z stats p R2
Gender 29 boys, 6 girls 28 boys, 4 girls 0.37 (0.7) 0.53 0.6 0.01
Age (months) 20.27 (19.78 20.93) 32.98 (31.03 34.74) 12.79 (0.98) 13.09 <0.001 0.72
MSEL-EL Raw scores: 19.89 (18.4 21.76) Raw scores: 17.56 (15.29 20.3) -2.32 (1.53) -1.52 0.13 0.03
MSEL-VR Raw scores: 26 (24.83 27.09) Raw scores: 26.91 (14.94 28.79) 0.91 (1.11) 0.82 0.4 0.01
ADOS Mod1 – total score 0.83 (0.43 1.34) 14.12 (12.84 15.47) 13.3 (0.7) 18.42 <0.001 0.84
Child word tokens 252.8 (194.1 331.05) 192.25 (126.83 280.61) -60.55 (50.01) -1.21 0.23 0.02
Child word types 55.09 (42.53 70.65) 51.19 (33.72 72.08) -3.9 (11.78) -0.33 0.74 0.02
Child MLU 1.38 (1.3 1.46) 1.36 (1.2 1.59) -0.02 (0.1) -0.19 0.85 0.00
Father’s education (years past 8th grade) 8.27 (7.39-9.21) 7.53 (6.59-8.5) 0.74 (0.67) t-stat: 1.11 0.271 0.02
Mother’s education (years past 8th grade) 7.91 (7.23-8.53) 7.89 (6.86-8.91) 0.02 (0.6) t-stat: 0.031 0.975 0.00

Modelling language development

We modelled language development using mixed-effects growth curves: multi-level models accounting for intercepts and slopes at individual and group levels, and including a non-linear term for changes over time.

\[ Linguistic Feature = \beta_{0i} + \] \[ \beta_{1i}Visit + \] \[ \beta_{2i}Visit^2 + \] \[ \beta_{3}Diagnosis + \] \[ \beta_{4}ExpressiveLanguage + \] \[ \beta_{5}VisualReception + \epsilon \]

Q1: Development of children's language

What do the longitudinal trajectories of children's language development look like?

Q1: Tokens (number of words)

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Q1: Types (vocabulary)

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Q1: MLU (complexity)

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Development of children's language

Word.tokens Word.types MLU
R2m, R2 R2m = 0.59, R2 = 0.82 R2m = 0.68, R2 = 0.88 R2m = 0.67, R2 = 0.79
Predictor
Intercept β = 571.90, SE = 29.47, t-stat = 19.41, p < 0.0001 β = 153.44, SE = 6.90, t-stat = 22.24, p < 0.0001 β = 2.21, SE = 0.05, t-stat = 43.02, p < 0.0001
Visit β = 272.65, SE = 29.56, t-stat = 9.22, p < 0.0001 β = 85.10, SE = 8.22, t-stat = 10.35, p < 0.0001 β = 0.59, SE = 0.06, t-stat = 9.19, p < 0.0001
Visit2 β = -23.34, SE = 3.97, t-stat = -5.88, p < 0.0001 β = -7.48, SE = 1.12, t-stat = -6.71, p < 0.0001 β = -0.04, SE = 0.01, t-stat = -4.43, p < 0.0001
Diagnosis β = -134.77, SE = 41.37, t-stat = -3.26, p = 0.0017 β = -44.51, SE = 9.99, t-stat = -4.46, p < 0.0001 β = -0.33, SE = 0.07, t-stat = -4.54, p = 0.0015
MSEL EL β = 22.09, SE = 5.74, t-stat = 3.85, p = 0.0003 β = 4.51, SE = 1.30, t-stat = 3.47, p = 0.0009 β = 0.04, SE = 0.01, t-stat = 3.57, p = 0.0007
Visit : Diagnosis β = -51.34, SE = 14.15, t-stat = -3.63, p = 0.0005 β = -47.35, SE = 12.05, t-stat = -3.93, p = 0.0002 β = -0.19, SE = 0.03, t-stat = -7.18, p < 0.0001
Visit : EL β = 9.99, SE = 4.86, t-stat = 2.06, p = 0.0422 β = 1.78, SE = 1.02, t-stat = 1.74, p = 0.0851 β = 0.04, SE = 0.01, t-stat = 3.69, p = 0.0003
Visit2 : Diagnosis Not included β = 4.30, SE = 1.63, t-stat = 2.63, p = 0.0102 Not Included
Visit2 : EL β = -2.16, SE = 0.63, t-stat = -3.41, p = 0.0010 β = -0.45, SE = 0.13, t-stat = -3.49, p = 0.0008 β = -0.01, SE = 0.00, t-stat = -4.97, p = 0.0001
Diagnosis : EL β = 18.84, SE = 6.94, t-stat = 2.71, p = 0.0084 β = 5.89, SE = 1.56, t-stat = 3.77, p = 0.0003 β = 0.06, SE = 0.01, t-stat = 4.76, p < 0.0001
Diagnosis : EL : Visit β = 8.65, SE = 2.37, t-stat = 3.65, p = 0.0005 β = 2.33, SE = 0.54, t-stat = 4.32, p = 0.0001 β = 0.02, SE = 0.00, t-stat = 3.52, p = 0.0007
Random Effects Child intercept SD: 162.82 Child intercept SD: 37.83 Child intercept SD: 0.26
Visit slope SD: 140.34 Visit slope SD: 29.85 Visit slope SD: 0.24
Visit2 slope SD: 18.77 Visit2 slope SD: 3.82 Visit2 slope SD: 0.04
Residual SD: 159.59 Residual SD: 32.25 Residual SD: 0.39

ADOS not an important predictor (BIC)

Word.tokens Word.types MLU
R2m, R2 R2m = 0.5, R2 = 0.81 R2m = 0.72, R2 = 0.91 R2m = 0.59, R2 = 0.75
Predictor
Intercept β = 372.71, SE = 26.30, t-stat = 14.17, p < 0.0001 β = 109.01, SE = 6.83, t-stat = 15.95, p < 0.0001 β = 1.84, SE = 0.06, t-stat = 31.70, p < 0.0001
ADOS Not Included Not Included Not Included
Visit β = 44.43, SE = 9.07, t-stat = 4.90, p < 0.0001 β = 37.82, SE = 8.29, t-stat = 4.56, p = 0.0001 β = 0.11, SE = 0.02, t-stat = 5.16, p < 0.0001
Visit2 Not Included β = -3.19, SE = 1.01, t-stat = -3.15, p = 0.0027 Not Included
MSEL EL β = 30.66, SE = 2.72, t-stat = 11.26, p < 0.0001 β = 10.41, SE = 0.93, t-stat = 11.19, p < 0.0001 β = 0.08, SE = 0.01, t-stat = 10.83, p < 0.0001
Visit : EL Not Included β = 4.15, SE = 1.13, t-stat = 3.68, p = 0.0007 Not Included
Visit2 : EL Not Included β = -0.46, SE = 0.14, t-stat = -3.35, p = 0.0015 Not Included
Random effects Child intercept SD: 165.62 Child intercept SD: 36.12 Child intercept SD: 0.28
Visit slope SD: 166.72 Visit slope SD: 31.18 Visit slope SD: 0.39
Visit2 slope SD: 17.61 Visit2 slope SD: 2.98 Visit2 slope SD: 0.05
Residual SD: 145.52 Residual SD: 28.69 Residual SD: 0.42

Q2: "Development" of parental language

What do the longitudinal trajectories of parents' language production look like?

Q2: Tokens (number of words)

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Q2: Types (vocabulary)

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Q2: MLU (complexity)

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Development of parents's language

Word.tokens Word.types MLU
R2m, R2 R2m = 0.11, R2 = 0.82 R2m = 0.07, R2 = 0.77 R2m = 0.27, R2 = 0.70
Predictor
Intercept β = 2284.70, SE = 67.43, t-stat = 33.88, p < 0.0001 β = 445.60, SE = 10.64, t-stat = 41.87, p < 0.0001 β = 3.27, SE = 0.31, t-stat = 10.52, p < 0.0001
Visit Not Included β = 17.17, SE = 2.01, t-stat = 8.55, p < 0.0001 β = 0.31, SE = 0.07, t-stat = 4.60, p < 0.0001
Visit2 Not Included Not Included β = -0.03, SE = 0.01, t-stat = -3.17, p = 0.0023
Diagnosis Not Included Not Included β = -0.54, SE = 0.10, t-stat = -5.21, p < 0.0001
Mullen Visual Reception (VR) β = 51.13, SE = 15.06, t-stat = 3.40, p = 0.0012 Not Included β = 0.04, SE = 0.01, t-stat = 3.15, p = 0.0025
Random Effects Child intercept SD: 547.10 Child intercept SD: 87.90 Child intercept SD: 0.41
Visit slope SD: 321.87 Visit slope SD: 45.59 Visit slope SD: 0.30
Visit2 slope SD: 38.16 Visit2 slope SD: 5.33 Visit2 slope SD: 0.03
Residual SD: 293.95 Residual SD: 52.44 Residual SD: 0.38

ADOS not an important predictor

Word.tokens Word.types MLU
R2m, R2 R2m = 0.17, R2 = 0.89 R2m = 0.06, R2 = 0.72 R2m = 0.03, R2 = 0.72
Predictor
Intercept β = 2248.71, SE = 111.26, t-stat = 20.21, p < 0.0001 β = 429.39, SE = 14.95, t-stat = 28.72, p < 0.0001 β = 3.64, SE = 0.09, t-stat = 40.04, p < 0.0001
ADOS Not Included Not Included Not Included
Visit β = -33.17, SE = 115.88, t-stat = -0.29, p = 0.7769 β = 15.08, SE = 3.02, t-stat = 5.00, p < 0.0001 β = 0.08, SE = 0.03, t-stat = 2.92, p = 0.0065
Visit2 β = 11.43, SE = 14.94, t-stat = 0.76, p = 0.4527 Not Included Not Included
MSEL VR β = 64.76, SE = 18.80, t-stat = 3.45, p = 0.0017 Not Included Not Included
Random effects Child intercept SD: 601.16 Child intercept SD: 84.86 Child intercept SD: 0.57
Visit slope SD: 439.02 Visit slope SD: 56.25 Visit slope SD: 0.47
Visit2 slope SD: 50.21 Visit2 slope SD: 6.37 Visit2 slope SD: 0.06
Residual SD: 262.11 Residual SD: 57.76 Residual SD: 0.40

Q3 Parent-child Linguistic Matching

Do parents and children match each other during conversation, and what factors play a role?

Q3: Parent-child matching (Tokens)

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Q3: Parent-child matching (Types)

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Q3: Parent-child matching (MLU)

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Parent-child matching

Parental.word.tokens Parental.word.types Parental.MLU
R2m, R2 R2m = 0.14, R2 = 0.79 R2m = 0.16, R2 = 0.75 R2m = 0.39, R2 = 0.72
Predictor
Intercept β = 2296.43, SE = 67.05, t-stat = 34.25, p < 0.0001 β = 451.00, SE = 10.27, t-stat = 43.92, p < 0.0001 β = 4.14, SE = 0.07, t-stat = 60.49, p < 0.0001
Child β = 0.32, SE = 0.08, t-stat = 4.15, p < 0.0001 β = 0.35, SE = 0.06, t-stat = 5.67, p < 0.0001 β = 0.36, SE = 0.04, t-stat = 9.49, p < 0.0001
Production
Diagnosis Not Included Not Included β = -0.40, SE = 0.10, t-stat = -3.92, p = 0.0002
Visit Not Included β = 9.81, SE = 2.41, t-stat = 4.06, p = 0.0001 Not Included
MSEL VR β = 39.83, SE = 15.17, t-stat = 2.63, p = 0.0106 Not Included β = 0.02, SE = 0.01, t-stat = 1.67, p = 0.0991
Child Not Included Not Included β = 0.03, SE = 0.01, t-stat = 3.63, p = 0.0003
Production : MSEL VR
Random effects Child intercept SD: 534.31 Child intercept SD: 81.11 Child intercept SD: 0.55
Visit slope SD: 85.61 Visit slope SD: 9.32 Visit slope SD: 0.11
Residual SD: 312.18 Residual SD: 53.33 Residual SD: 0.37

Influence of parental language on later child language

Child.word.tokens Child.word.types Child.MLU
R2m, R2 R2m = 0.63, R2 = 0.70 R2m = 0.73, R2 = 0.78 R2m = 0.64, R2 = 0.66
Predictor
Intercept β = 598.42, SE = 22.72, t-stat = 26.33, p < 0.0001 β = 155.51, SE = 4.87, t-stat = 31.95, p < 0.0001 β = 2.24, SE = 0.04, t-stat = 51.70, p < 0.0001
Diagnosis β = -93.28, SE = 34.19, t-stat = -2.73, p = 0.0089 β = -24.67, SE = 7.30, t-stat = -3.38, p = 0.0013 β = -0.20, SE = 0.07, t-stat = -3.06, p = 0.0029
Mullen expressive language (EL) β = 10.70, SE = 4.55, t-stat = 2.35, p = 0.023 β = 1.72, SE = 0.97, t-stat = 1.77, p = 0.0814 β = 0.03, SE = 0.01, t-stat = 3.09, p = 0.0026
Diagnosis : EL β = 12.80, SE = 5.50, t-stat = 2.33, p = 0.0247 β = 3.42, SE = 1.18, t-stat = 2.89, p = 0.0056 β = 0.02, SE = 0.01, t-stat = 2.05, p = 0.0431
Child performance in the previous visit (same index as the outcome) β = 0.41, SE = 0.05, t-stat = 8.91, p < 0.0001 β = 3.51, SE = 1.18, t-stat = 2.97, p = 0.0045 β = 0.49, SE = 0.05, t-stat = 9.92, p < 0.0001
Parental word tokens in the previous visit Not Included Not Included Not Included
Parental word types in the previous visit Not Included Not Included Not Included
Parental MLU in the previous visit β = 88.37, SE = 21.59, t-stat = 4.09, p = 0.0001 β = 17.60, SE = 4.74, t-stat = 3.72, p = 0.0002 β = 0.09, SE = 0.04, t-stat = 1.85, p = 0.0809
Random Effects Child intercept SD: 95.16 Child intercept SD: 20.65 Child intercept SD: 0.21
Residual SD: 204.95 Residual SD: 43.34 Residual SD: 0.37

Influence of children's language on later parent language

Parental.word.tokens Parental.word.types Parental.MLU
R2m, R2 R2m = 0.61, R2 = 0.64 R2m = 0.2, R2 = 0.62 R2m = 0.49, R2 = 0.53
Predictor
Intercept β = 2334.06, SE = 25.31, t-stat = 92.22, p < 0.0001 β = 462.65, SE = 7.91, t-stat = 58.45, p < 0.0001 β = 4.19, SE = 0.04, t-stat = 100.08, p < 0.0001
Diagnosis Not Included Not Included β = -0.22, SE = 0.07, t-stat = -3.41, p = 0.003
Parental performance in the previous visit (same index as the outcome) β = 0.74, SE = 0.04, t-stat = 20.92, p < 0.0001 β = 0.34, SE = 0.05, t-stat = 6.91, p < 0.0001 β = 0.47, SE = 0.04, t-stat = 10.52, p < 0.0001
Child word tokens Not Included Not Included β = 0.00001, SE = 0.0001, t-stat = 0.01, p = 0.99
Child word types Not Included Not Included Not Included
Child MLU Not Included β = 16.6, SE = 5.45, t-stat = 3.04, p = 0.0025 Not Included
Child word tokens: Diagnosis Not Included Not Included β = 0.001, SE = 0.0002, t-stat = 3.94, p = 0.0002
Random effects Child Intercept SD: 111.78 Child Intercept SD: 59.81 Child Intercept SD: 0.13
Residual SD: 389.39 Residual SD: 57.17 Residual SD: 0.46

Our 6 major findings

  • Children’s production of word types, tokens, and MLU increased across visits, and were predicted by their Expressive Language EL (positively) and diagnosis (negatively) from Visit 1.
  • Parents’ production also increased across visits, and was predicted by their child’s nonverbal cognition (positively) and diagnosis (negatively) from Visit 1.
  • At all visits and across groups, children and parents matched each other in lexical and syntactic production.

Our 6 major findings

  • Parents who produced longer MLUs during a given visit had children who produced more word types and tokens, and had longer MLUs, at the subsequent visit.
  • When both child EL at Visit 1 and parent MLU were included in the model, both contributed significantly to future child language. However, EL accounted for a greater proportion of the variance.
  • Finally, children’s speech significantly predicted parent speech at the next visit.

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Data and code are available online at OSF